# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.initializer import MSRA from paddle.fluid.param_attr import ParamAttr from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear from model import Model from .download import get_weights_path __all__ = ['MobileNetV1', 'mobilenet_v1'] model_urls = { 'mobilenetv1_1.0': ('https://paddle-hapi.bj.bcebos.com/models/mobilenet_v1_x1.0.pdparams', 'bf0d25cb0bed1114d9dac9384ce2b4a6') } class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, num_channels, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='relu', use_cudnn=True, name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=num_groups, act=None, use_cudnn=use_cudnn, param_attr=ParamAttr( initializer=MSRA(), name=self.full_name() + "_weights"), bias_attr=False) self._batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(name=self.full_name() + "_bn" + "_scale"), bias_attr=ParamAttr(name=self.full_name() + "_bn" + "_offset"), moving_mean_name=self.full_name() + "_bn" + '_mean', moving_variance_name=self.full_name() + "_bn" + '_variance') def forward(self, inputs): y = self._conv(inputs) y = self._batch_norm(y) return y class DepthwiseSeparable(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters1, num_filters2, num_groups, stride, scale, name=None): super(DepthwiseSeparable, self).__init__() self._depthwise_conv = ConvBNLayer( num_channels=num_channels, num_filters=int(num_filters1 * scale), filter_size=3, stride=stride, padding=1, num_groups=int(num_groups * scale), use_cudnn=False) self._pointwise_conv = ConvBNLayer( num_channels=int(num_filters1 * scale), filter_size=1, num_filters=int(num_filters2 * scale), stride=1, padding=0) def forward(self, inputs): y = self._depthwise_conv(inputs) y = self._pointwise_conv(y) return y class MobileNetV1(Model): """MobileNetV1 model from `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" `_. Args: scale (float): scale of channels in each layer. Default: 1.0. num_classes (int): output dim of last fc layer. Default: -1. with_pool (bool): use pool or not. Default: False. classifier_activation (str): activation for the last fc layer. Default: 'softmax'. """ def __init__(self, scale=1.0, num_classes=-1, with_pool=False, classifier_activation='softmax'): super(MobileNetV1, self).__init__() self.scale = scale self.dwsl = [] self.num_classes = num_classes self.with_pool = with_pool self.conv1 = ConvBNLayer( num_channels=3, filter_size=3, channels=3, num_filters=int(32 * scale), stride=2, padding=1) dws21 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(32 * scale), num_filters1=32, num_filters2=64, num_groups=32, stride=1, scale=scale), name="conv2_1") self.dwsl.append(dws21) dws22 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(64 * scale), num_filters1=64, num_filters2=128, num_groups=64, stride=2, scale=scale), name="conv2_2") self.dwsl.append(dws22) dws31 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=128, num_groups=128, stride=1, scale=scale), name="conv3_1") self.dwsl.append(dws31) dws32 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=256, num_groups=128, stride=2, scale=scale), name="conv3_2") self.dwsl.append(dws32) dws41 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=256, num_groups=256, stride=1, scale=scale), name="conv4_1") self.dwsl.append(dws41) dws42 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=512, num_groups=256, stride=2, scale=scale), name="conv4_2") self.dwsl.append(dws42) for i in range(5): tmp = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=512, num_groups=512, stride=1, scale=scale), name="conv5_" + str(i + 1)) self.dwsl.append(tmp) dws56 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=1024, num_groups=512, stride=2, scale=scale), name="conv5_6") self.dwsl.append(dws56) dws6 = self.add_sublayer( sublayer=DepthwiseSeparable( num_channels=int(1024 * scale), num_filters1=1024, num_filters2=1024, num_groups=1024, stride=1, scale=scale), name="conv6") self.dwsl.append(dws6) if with_pool: self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True) if num_classes > -1: self.out = Linear( int(1024 * scale), num_classes, act=classifier_activation, param_attr=ParamAttr( initializer=MSRA(), name=self.full_name() + "fc7_weights"), bias_attr=ParamAttr(name="fc7_offset")) def forward(self, inputs): y = self.conv1(inputs) for dws in self.dwsl: y = dws(y) if self.with_pool: y = self.pool2d_avg(y) if self.num_classes > 0: y = fluid.layers.reshape(y, shape=[-1, 1024]) y = self.out(y) return y def _mobilenet(arch, pretrained=False, **kwargs): model = MobileNetV1(num_classes=1000, with_pool=True, **kwargs) if pretrained: assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format( arch) weight_path = get_weights_path(model_urls[arch][0], model_urls[arch][1]) assert weight_path.endswith( '.pdparams'), "suffix of weight must be .pdparams" model.load(weight_path[:-9]) return model def mobilenet_v1(pretrained=False, scale=1.0): """MobileNetV1 Args: pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False. scale: (float): scale of channels in each layer. Default: 1.0. """ model = _mobilenet('mobilenetv1_' + str(scale), pretrained, scale=scale) return model